Performance of conventional automatic speech recognition systems, which uses only the acoustic signal, is severely degraded by acoustic noise. The myoelectric signal from articulatory muscles of the face is proposed as a secondary source of speech information to enhance conventional automatic speech recognition systems. An acoustic speech expert and myoelectric speech expert are combined using a novel approach based on evidence theory. Data were collected from 5 subjects across an 18 dB range of noise levels. The classification rate of the acoustic expert decreased dramatically with noise, while the myoelectric signal expert remained relatively unaffected by the noise. This method of combining experts is able to dynamically track the reliability of experts. Classification rates of the multi-expert system were better or near either individual expert at all noise levels.

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Keywords Evidence theory, Hidden Markov models, Multi-expert system, Myoelectric signal, Speech recognition
Conference Proceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS)
Chan, A, Englehart, K. (K.), Hudgins, B. (B.), & Lovely, D.F. (D. F.). (2002). A multi-expert speech recognition system using acoustic and myoelectric signals. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (pp. 72–73).